2014/02/11 by Wen‐Yu Hua, Debashis Ghosh, Hua, Wen-Yu +1 · 1 citation
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #FOS: Computer and information sciences #Gene expression and cancer classification #Genetic Associations and Epidemiology #Machine Learning (stat.ML) #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.1402.2679
openalex publication_date 2014/02/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Associating genetic markers with a multidimensional phenotype is an important yet challenging problem. In this work, we establish the equivalence between two popular methods: kernel-machine regression (KMR), and kernel distance covariance (KDC). KMR is a semiparametric regression frameworks that models the covariate effects parametrically, while the genetic markers are considered non-parametrically. KDC represents a class of methods that includes distance covariance (DC) and Hilbert-Schmidt Independence Criterion (HSIC), which are nonparametric tests of independence. We show the equivalence between the score test of KMR and the KDC statistic under certain conditions. This result leads to a novel generalization of the KDC test that incorporates the covariates. Our contributions are three-fold: (1) establishing the equivalence between KMR and KDC; (2) showing that the principles of kernel machine regression can be applied to the interpretation of KDC; (3) the development of a broader class of KDC statistics, that the members are the quantities of different kernels. We demonstrate the proposals using simulation studies. Data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) is used to explore the association between the genetic variants on gene FLJ16124 and phenotypes represented in 3D structural brain MR images adjusting for age and gender. The results suggest that SNPs of FLJ16124 exhibit strong pairwise interaction effects that are correlated to the changes of brain region volumes.